Enterprise Resource Planning (ERP) software, Customer Relations Management (CRM) Software, and custom software applications support businesses operations and provide a wealth of business information. While processing transactions and collecting more customer data is valuable for a business, unmanaged data growth can slow application performance, strain financial and technical resources and jeopardize completing business-critical processes on time.
In order to avoid the negative impact of unmanaged growth in very large databases, many software solutions exist to provide archiving capabilities for databases. Archiving software enables users to segregate historical data from current data and store it securely and cost-effectively.
By reducing the amount of information in a production database, less disk space is used for application data and this can cut storage costs. As a result of archiving, there can be less information to search through and applications execute and process data faster. When databases run more efficiently, then organizations derive the most business value from mission-critical applications.
Simply archiving data that is explicitly referenced between tables in a database may not be enough when there are extensive data relationships defined by database applications, such as ERP software. One challenge in managing the archiving of inactive but important data is ensuring that all the relevant data is archived together. Achieving the goal of archiving relevant data together helps ensure that interrelated data is not separated to different storage tiers, and having complete archive data makes it possible to restore interrelated data together. Without this completeness of archived data, the archived data that is restored for use by the application may be missing, which results in corrupt and potentially unusable data. There is also the possibility that database applications can unknowingly produce incorrect results using incomplete data retrieved from the archive.
Given a set of driving rows from a table, finding all the interrelated data in the database is a huge challenge. For database applications, this problem is further compounded by the fact that data relationships are not represented completely by the explicit relationships defined in the database tables. Oftentimes, the database applications determine how data is implicitly interrelated.